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Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

Showing 276300 of 1718 papers

TitleStatusHype
Delay-Aware Multi-Agent Reinforcement Learning for Cooperative and Competitive EnvironmentsCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Multi-Step Reinforcement Learning for Single Image Super-ResolutionCode1
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
Collaborating with Humans without Human DataCode1
Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement LearningCode1
Neural Auto-Curricula in Two-Player Zero-Sum GamesCode1
Multi-Agent Collaboration via Reward Attribution DecompositionCode1
Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication MethodCode1
Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics ScenesCode1
Distributed Resource Allocation with Multi-Agent Deep Reinforcement Learning for 5G-V2V CommunicationCode1
"Other-Play" for Zero-Shot CoordinationCode1
Reinforced Prompt Personalization for Recommendation with Large Language ModelsCode1
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information PrinciplesCode1
Modelling crypto markets by multi-agent reinforcement learningCode0
Modeling Moral Choices in Social Dilemmas with Multi-Agent Reinforcement LearningCode0
Modelling Opaque Bilateral Market Dynamics in Financial Trading: Insights from a Multi-Agent Simulation StudyCode0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?Code0
Solving Dynamic Principal-Agent Problems with a Rationally Inattentive PrincipalCode0
MolOpt: Autonomous Molecular Geometry Optimization using Multi-Agent Reinforcement LearningCode0
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic ScenarioCode0
An Organizationally-Oriented Approach to Enhancing Explainability and Control in Multi-Agent Reinforcement LearningCode0
Measuring Policy Distance for Multi-Agent Reinforcement LearningCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified